---
_id: '2057'
abstract:
- lang: eng
text: 'In the past few years, a lot of attention has been devoted to multimedia
indexing by fusing multimodal informations. Two kinds of fusion schemes are generally
considered: The early fusion and the late fusion. We focus on late classifier
fusion, where one combines the scores of each modality at the decision level.
To tackle this problem, we investigate a recent and elegant well-founded quadratic
program named MinCq coming from the machine learning PAC-Bayesian theory. MinCq
looks for the weighted combination, over a set of real-valued functions seen as
voters, leading to the lowest misclassification rate, while maximizing the voters’
diversity. We propose an extension of MinCq tailored to multimedia indexing. Our
method is based on an order-preserving pairwise loss adapted to ranking that allows
us to improve Mean Averaged Precision measure while taking into account the diversity
of the voters that we want to fuse. We provide evidence that this method is naturally
adapted to late fusion procedures and confirm the good behavior of our approach
on the challenging PASCAL VOC’07 benchmark.'
alternative_title:
- LNCS
arxiv: 1
author:
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
- first_name: Amaury
full_name: Habrard, Amaury
last_name: Habrard
- first_name: Stéphane
full_name: Ayache, Stéphane
last_name: Ayache
citation:
ama: 'Morvant E, Habrard A, Ayache S. Majority vote of diverse classifiers for late
fusion. In: Lecture Notes in Computer Science (Including Subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol
8621. Springer; 2014:153-162. doi:10.1007/978-3-662-44415-3_16'
apa: 'Morvant, E., Habrard, A., & Ayache, S. (2014). Majority vote of diverse
classifiers for late fusion. In Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8621, pp. 153–162). Joensuu, Finland: Springer. https://doi.org/10.1007/978-3-662-44415-3_16'
chicago: Morvant, Emilie, Amaury Habrard, and Stéphane Ayache. “Majority Vote of
Diverse Classifiers for Late Fusion.” In Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), 8621:153–62. Springer, 2014. https://doi.org/10.1007/978-3-662-44415-3_16.
ieee: E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers
for late fusion,” in Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Joensuu, Finland, 2014, vol. 8621, pp. 153–162.
ista: 'Morvant E, Habrard A, Ayache S. 2014. Majority vote of diverse classifiers
for late fusion. Lecture Notes in Computer Science (including subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). IAPR: International
Workshop on Structural, Syntactic, and Statistical Pattern Recognition, LNCS,
vol. 8621, 153–162.'
mla: Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.”
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014,
pp. 153–62, doi:10.1007/978-3-662-44415-3_16.
short: E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics), Springer, 2014, pp. 153–162.
conference:
end_date: 2014-08-22
location: Joensuu, Finland
name: 'IAPR: International Workshop on Structural, Syntactic, and Statistical Pattern
Recognition'
start_date: 2014-08-20
date_created: 2018-12-11T11:55:28Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2021-01-12T06:55:01Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-662-44415-3_16
ec_funded: 1
external_id:
arxiv:
- '1404.7796'
intvolume: ' 8621'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1404.7796
month: '01'
oa: 1
oa_version: Preprint
page: 153 - 162
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '4989'
quality_controlled: '1'
scopus_import: 1
status: public
title: Majority vote of diverse classifiers for late fusion
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8621
year: '2014'
...
---
_id: '2160'
abstract:
- lang: eng
text: Transfer learning has received a lot of attention in the machine learning
community over the last years, and several effective algorithms have been developed.
However, relatively little is known about their theoretical properties, especially
in the setting of lifelong learning, where the goal is to transfer information
to tasks for which no data have been observed so far. In this work we study lifelong
learning from a theoretical perspective. Our main result is a PAC-Bayesian generalization
bound that offers a unified view on existing paradigms for transfer learning,
such as the transfer of parameters or the transfer of low-dimensional representations.
We also use the bound to derive two principled lifelong learning algorithms, and
we show that these yield results comparable with existing methods.
article_processing_charge: No
author:
- first_name: Anastasia
full_name: Pentina, Anastasia
id: 42E87FC6-F248-11E8-B48F-1D18A9856A87
last_name: Pentina
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Pentina A, Lampert C. A PAC-Bayesian bound for Lifelong Learning. In: Vol
32. ML Research Press; 2014:991-999.'
apa: 'Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning
(Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine
Learning, Beijing, China: ML Research Press.'
chicago: Pentina, Anastasia, and Christoph Lampert. “A PAC-Bayesian Bound for Lifelong
Learning,” 32:991–99. ML Research Press, 2014.
ieee: 'A. Pentina and C. Lampert, “A PAC-Bayesian bound for Lifelong Learning,”
presented at the ICML: International Conference on Machine Learning, Beijing,
China, 2014, vol. 32, pp. 991–999.'
ista: 'Pentina A, Lampert C. 2014. A PAC-Bayesian bound for Lifelong Learning. ICML:
International Conference on Machine Learning vol. 32, 991–999.'
mla: Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong
Learning. Vol. 32, ML Research Press, 2014, pp. 991–99.
short: A. Pentina, C. Lampert, in:, ML Research Press, 2014, pp. 991–999.
conference:
end_date: 2014-06-26
location: Beijing, China
name: 'ICML: International Conference on Machine Learning'
start_date: 2014-06-21
date_created: 2018-12-11T11:56:03Z
date_published: 2014-05-10T00:00:00Z
date_updated: 2023-10-17T11:54:24Z
day: '10'
department:
- _id: ChLa
intvolume: ' 32'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?id=3045003
month: '05'
oa: 1
oa_version: Submitted Version
page: 991 - 999
publication_status: published
publisher: ML Research Press
publist_id: '4844'
quality_controlled: '1'
scopus_import: '1'
status: public
title: A PAC-Bayesian bound for Lifelong Learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2014'
...
---
_id: '2171'
abstract:
- lang: eng
text: We present LS-CRF, a new method for training cyclic Conditional Random Fields
(CRFs) from large datasets that is inspired by classical closed-form expressions
for the maximum likelihood parameters of a generative graphical model with tree
topology. Training a CRF with LS-CRF requires only solving a set of independent
regression problems, each of which can be solved efficiently in closed form or
by an iterative solver. This makes LS-CRF orders of magnitude faster than classical
CRF training based on probabilistic inference, and at the same time more flexible
and easier to implement than other approximate techniques, such as pseudolikelihood
or piecewise training. We apply LS-CRF to the task of semantic image segmentation,
showing that it achieves on par accuracy to other training techniques at higher
speed, thereby allowing efficient CRF training from very large training sets.
For example, training a linearly parameterized pairwise CRF on 150,000 images
requires less than one hour on a modern workstation.
alternative_title:
- LNCS
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Matthieu
full_name: Guillaumin, Matthieu
last_name: Guillaumin
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. Closed-form approximate
CRF training for scalable image segmentation. In: Fleet D, Pajdla T, Schiele B,
Tuytelaars T, eds. Lecture Notes in Computer Science (Including Subseries Lecture
Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol
8691. Springer; 2014:550-565. doi:10.1007/978-3-319-10578-9_36'
apa: 'Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form
approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla,
B. Schiele, & T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including
subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36'
chicago: Kolesnikov, Alexander, Matthieu Guillaumin, Vittorio Ferrari, and Christoph
Lampert. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David
Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 8691:550–65. Springer,
2014. https://doi.org/10.1007/978-3-319-10578-9_36.
ieee: A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate
CRF training for scalable image segmentation,” in Lecture Notes in Computer
Science (including subseries Lecture Notes in Artificial Intelligence and Lecture
Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3,
pp. 550–565.
ista: 'Kolesnikov A, Guillaumin M, Ferrari V, Lampert C. 2014. Closed-form approximate
CRF training for scalable image segmentation. Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes
in Bioinformatics). ECCV: European Conference on Computer Vision, LNCS, vol. 8691,
550–565.'
mla: Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable
Image Segmentation.” Lecture Notes in Computer Science (Including Subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65,
doi:10.1007/978-3-319-10578-9_36.
short: A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla,
B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
Springer, 2014, pp. 550–565.
conference:
end_date: 2014-09-12
location: Zurich, Switzerland
name: 'ECCV: European Conference on Computer Vision'
start_date: 2014-09-06
date_created: 2018-12-11T11:56:07Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-3-319-10578-9_36
ec_funded: 1
editor:
- first_name: David
full_name: Fleet, David
last_name: Fleet
- first_name: Tomas
full_name: Pajdla, Tomas
last_name: Pajdla
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Tinne
full_name: Tuytelaars, Tinne
last_name: Tuytelaars
intvolume: ' 8691'
issue: PART 3
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1403.7057
month: '09'
oa: 1
oa_version: Submitted Version
page: 550 - 565
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '4813'
quality_controlled: '1'
scopus_import: 1
status: public
title: Closed-form approximate CRF training for scalable image segmentation
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 8691
year: '2014'
...
---
_id: '2172'
abstract:
- lang: eng
text: Fisher Kernels and Deep Learning were two developments with significant impact
on large-scale object categorization in the last years. Both approaches were shown
to achieve state-of-the-art results on large-scale object categorization datasets,
such as ImageNet. Conceptually, however, they are perceived as very different
and it is not uncommon for heated debates to spring up when advocates of both
paradigms meet at conferences or workshops. In this work, we emphasize the similarities
between both architectures rather than their differences and we argue that such
a unified view allows us to transfer ideas from one domain to the other. As a
concrete example we introduce a method for learning a support vector machine classifier
with Fisher kernel at the same time as a task-specific data representation. We
reinterpret the setting as a multi-layer feed forward network. Its final layer
is the classifier, parameterized by a weight vector, and the two previous layers
compute Fisher vectors, parameterized by the coefficients of a Gaussian mixture
model. We introduce a gradient descent based learning algorithm that, in contrast
to other feature learning techniques, is not just derived from intuition or biological
analogy, but has a theoretical justification in the framework of statistical learning
theory. Our experiments show that the new training procedure leads to significant
improvements in classification accuracy while preserving the modularity and geometric
interpretability of a support vector machine setup.
author:
- first_name: Vladyslav
full_name: Sydorov, Vladyslav
last_name: Sydorov
- first_name: Mayu
full_name: Sakurada, Mayu
last_name: Sakurada
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sydorov V, Sakurada M, Lampert C. Deep Fisher Kernels – End to end learning
of the Fisher Kernel GMM parameters. In: Proceedings of the IEEE Computer Society
Conference on Computer Vision and Pattern Recognition. IEEE; 2014:1402-1409.
doi:10.1109/CVPR.2014.182'
apa: 'Sydorov, V., Sakurada, M., & Lampert, C. (2014). Deep Fisher Kernels –
End to end learning of the Fisher Kernel GMM parameters. In Proceedings of
the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
(pp. 1402–1409). Columbus, USA: IEEE. https://doi.org/10.1109/CVPR.2014.182'
chicago: Sydorov, Vladyslav, Mayu Sakurada, and Christoph Lampert. “Deep Fisher
Kernels – End to End Learning of the Fisher Kernel GMM Parameters.” In Proceedings
of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,
1402–9. IEEE, 2014. https://doi.org/10.1109/CVPR.2014.182.
ieee: V. Sydorov, M. Sakurada, and C. Lampert, “Deep Fisher Kernels – End to end
learning of the Fisher Kernel GMM parameters,” in Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, Columbus, USA,
2014, pp. 1402–1409.
ista: 'Sydorov V, Sakurada M, Lampert C. 2014. Deep Fisher Kernels – End to end
learning of the Fisher Kernel GMM parameters. Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition. CVPR: Computer
Vision and Pattern Recognition, 1402–1409.'
mla: Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the
Fisher Kernel GMM Parameters.” Proceedings of the IEEE Computer Society Conference
on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.
short: V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp.
1402–1409.
conference:
end_date: 2014-06-28
location: Columbus, USA
name: 'CVPR: Computer Vision and Pattern Recognition'
start_date: 2014-06-23
date_created: 2018-12-11T11:56:08Z
date_published: 2014-09-24T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '24'
department:
- _id: ChLa
doi: 10.1109/CVPR.2014.182
ec_funded: 1
language:
- iso: eng
month: '09'
oa_version: None
page: 1402 - 1409
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the IEEE Computer Society Conference on Computer Vision
and Pattern Recognition
publication_status: published
publisher: IEEE
publist_id: '4812'
quality_controlled: '1'
scopus_import: 1
status: public
title: Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
type: conference
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '2173'
abstract:
- lang: eng
text: "In this work we introduce a new approach to co-classification, i.e. the task
of jointly classifying multiple, otherwise independent, data samples. The method
we present, named CoConut, is based on the idea of adding a regularizer in the
label space to encode certain priors on the resulting labelings. A regularizer
that encourages labelings that are smooth across the test set, for instance, can
be seen as a test-time variant of the cluster assumption, which has been proven
useful at training time in semi-supervised learning. A regularizer that introduces
a preference for certain class proportions can be regarded as a prior distribution
on the class labels. CoConut can build on existing classifiers without making
any assumptions on how they were obtained and without the need to re-train them.
The use of a regularizer adds a new level of flexibility. It allows the integration
of potentially new information at test time, even in other modalities than what
the classifiers were trained on. We evaluate our framework on six datasets, reporting
a clear performance gain in classification accuracy compared to the standard classification
setup that predicts labels for each test sample separately.\r\n"
author:
- first_name: Sameh
full_name: Khamis, Sameh
last_name: Khamis
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Khamis S, Lampert C. CoConut: Co-classification with output space regularization.
In: Proceedings of the British Machine Vision Conference 2014. BMVA Press;
2014.'
apa: 'Khamis, S., & Lampert, C. (2014). CoConut: Co-classification with output
space regularization. In Proceedings of the British Machine Vision Conference
2014. Nottingham, UK: BMVA Press.'
chicago: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with
Output Space Regularization.” In Proceedings of the British Machine Vision
Conference 2014. BMVA Press, 2014.'
ieee: 'S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,”
in Proceedings of the British Machine Vision Conference 2014, Nottingham,
UK, 2014.'
ista: 'Khamis S, Lampert C. 2014. CoConut: Co-classification with output space regularization.
Proceedings of the British Machine Vision Conference 2014. BMVC: British Machine
Vision Conference.'
mla: 'Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output
Space Regularization.” Proceedings of the British Machine Vision Conference
2014, BMVA Press, 2014.'
short: S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference
2014, BMVA Press, 2014.
conference:
end_date: 2014-09-05
location: Nottingham, UK
name: 'BMVC: British Machine Vision Conference'
start_date: 2014-09-01
date_created: 2018-12-11T11:56:08Z
date_published: 2014-09-01T00:00:00Z
date_updated: 2021-01-12T06:55:46Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
ec_funded: 1
file:
- access_level: open_access
checksum: c4c6d3efdb8ee648faf3e76849839ce2
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:08:23Z
date_updated: 2020-07-14T12:45:31Z
file_id: '4683'
file_name: IST-2016-490-v1+1_khamis-bmvc2014.pdf
file_size: 408172
relation: main_file
file_date_updated: 2020-07-14T12:45:31Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the British Machine Vision Conference 2014
publication_status: published
publisher: BMVA Press
publist_id: '4811'
pubrep_id: '490'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'CoConut: Co-classification with output space regularization'
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '2180'
abstract:
- lang: eng
text: Weighted majority votes allow one to combine the output of several classifiers
or voters. MinCq is a recent algorithm for optimizing the weight of each voter
based on the minimization of a theoretical bound over the risk of the vote with
elegant PAC-Bayesian generalization guarantees. However, while it has demonstrated
good performance when combining weak classifiers, MinCq cannot make use of the
useful a priori knowledge that one may have when using a mixture of weak and strong
voters. In this paper, we propose P-MinCq, an extension of MinCq that can incorporate
such knowledge in the form of a constraint over the distribution of the weights,
along with general proofs of convergence that stand in the sample compression
setting for data-dependent voters. The approach is applied to a vote of k-NN classifiers
with a specific modeling of the voters' performance. P-MinCq significantly outperforms
the classic k-NN classifier, a symmetric NN and MinCq using the same voters. We
show that it is also competitive with LMNN, a popular metric learning algorithm,
and that combining both approaches further reduces the error.
acknowledgement: 'This work was funded by the French project SoLSTiCe ANR-13-BS02-01
of the ANR. '
author:
- first_name: Aurélien
full_name: Bellet, Aurélien
last_name: Bellet
- first_name: Amaury
full_name: Habrard, Amaury
last_name: Habrard
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
- first_name: Marc
full_name: Sebban, Marc
last_name: Sebban
citation:
ama: Bellet A, Habrard A, Morvant E, Sebban M. Learning a priori constrained weighted
majority votes. Machine Learning. 2014;97(1-2):129-154. doi:10.1007/s10994-014-5462-z
apa: Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori
constrained weighted majority votes. Machine Learning. Springer. https://doi.org/10.1007/s10994-014-5462-z
chicago: Bellet, Aurélien, Amaury Habrard, Emilie Morvant, and Marc Sebban. “Learning
a Priori Constrained Weighted Majority Votes.” Machine Learning. Springer,
2014. https://doi.org/10.1007/s10994-014-5462-z.
ieee: A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained
weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer,
pp. 129–154, 2014.
ista: Bellet A, Habrard A, Morvant E, Sebban M. 2014. Learning a priori constrained
weighted majority votes. Machine Learning. 97(1–2), 129–154.
mla: Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”
Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z.
short: A. Bellet, A. Habrard, E. Morvant, M. Sebban, Machine Learning 97 (2014)
129–154.
date_created: 2018-12-11T11:56:10Z
date_published: 2014-10-01T00:00:00Z
date_updated: 2021-01-12T06:55:49Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/s10994-014-5462-z
ec_funded: 1
intvolume: ' 97'
issue: 1-2
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.archives-ouvertes.fr/hal-01009578/document
month: '10'
oa: 1
oa_version: Submitted Version
page: 129 - 154
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Machine Learning
publication_status: published
publisher: Springer
publist_id: '4802'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning a priori constrained weighted majority votes
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2014'
...
---
_id: '2189'
abstract:
- lang: fre
text: En apprentissage automatique, nous parlons d'adaptation de domaine lorsque
les données de test (cibles) et d'apprentissage (sources) sont générées selon
différentes distributions. Nous devons donc développer des algorithmes de classification
capables de s'adapter à une nouvelle distribution, pour laquelle aucune information
sur les étiquettes n'est disponible. Nous attaquons cette problématique sous l'angle
de l'approche PAC-Bayésienne qui se focalise sur l'apprentissage de modèles définis
comme des votes de majorité sur un ensemble de fonctions. Dans ce contexte, nous
introduisons PV-MinCq une version adaptative de l'algorithme (non adaptatif) MinCq.
PV-MinCq suit le principe suivant. Nous transférons les étiquettes sources aux
points cibles proches pour ensuite appliquer MinCq sur l'échantillon cible ``auto-étiqueté''
(justifié par une borne théorique). Plus précisément, nous définissons un auto-étiquetage
non itératif qui se focalise dans les régions où les distributions marginales
source et cible sont les plus similaires. Dans un second temps, nous étudions
l'influence de notre auto-étiquetage pour en déduire une procédure de validation
des hyperparamètres. Finalement, notre approche montre des résultats empiriques
prometteurs.
article_processing_charge: No
author:
- first_name: Emilie
full_name: Morvant, Emilie
id: 4BAC2A72-F248-11E8-B48F-1D18A9856A87
last_name: Morvant
orcid: 0000-0002-8301-7240
citation:
ama: 'Morvant E. Adaptation de domaine de vote de majorité par auto-étiquetage non
itératif. In: Vol 1. Elsevier; 2014:49-58.'
apa: 'Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone
sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne,
France: Elsevier.'
chicago: Morvant, Emilie. “Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif,” 1:49–58. Elsevier, 2014.
ieee: 'E. Morvant, “Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif,” presented at the CAP: Conférence Francophone sur l’Apprentissage
Automatique (Machine Learning French Conference), Saint-Etienne, France, 2014,
vol. 1, pp. 49–58.'
ista: 'Morvant E. 2014. Adaptation de domaine de vote de majorité par auto-étiquetage
non itératif. CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine
Learning French Conference) vol. 1, 49–58.'
mla: Morvant, Emilie. Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage
Non Itératif. Vol. 1, Elsevier, 2014, pp. 49–58.
short: E. Morvant, in:, Elsevier, 2014, pp. 49–58.
conference:
location: Saint-Etienne, France
name: 'CAP: Conférence Francophone sur l''Apprentissage Automatique (Machine Learning
French Conference)'
date_created: 2018-12-11T11:56:13Z
date_published: 2014-07-01T00:00:00Z
date_updated: 2021-01-12T06:55:52Z
day: '01'
department:
- _id: ChLa
intvolume: ' 1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://hal.archives-ouvertes.fr/hal-01005776/
month: '07'
oa: 1
oa_version: Preprint
page: 49-58
publication_status: published
publisher: Elsevier
publist_id: '4785'
quality_controlled: '1'
status: public
title: Adaptation de domaine de vote de majorité par auto-étiquetage non itératif
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2014'
...
---
_id: '2516'
abstract:
- lang: eng
text: 'We study the problem of object recognition for categories for which we have
no training examples, a task also called zero-data or zero-shot learning. This
situation has hardly been studied in computer vision research, even though it
occurs frequently: the world contains tens of thousands of different object classes
and for only few of them image collections have been formed and suitably annotated.
To tackle the problem we introduce attribute-based classification: objects are
identified based on a high-level description that is phrased in terms of semantic
attributes, such as the object''s color or shape. Because the identification of
each such property transcends the specific learning task at hand, the attribute
classifiers can be pre-learned independently, e.g. from existing image datasets
unrelated to the current task. Afterwards, new classes can be detected based on
their attribute representation, without the need for a new training phase. In
this paper we also introduce a new dataset, Animals with Attributes, of over 30,000
images of 50 animal classes, annotated with 85 semantic attributes. Extensive
experiments on this and two more datasets show that attribute-based classification
indeed is able to categorize images without access to any training images of the
target classes.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Hannes
full_name: Nickisch, Hannes
last_name: Nickisch
- first_name: Stefan
full_name: Harmeling, Stefan
last_name: Harmeling
citation:
ama: Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot
learning of object categories. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140
apa: Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification
for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2013.140
chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based
Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2013. https://doi.org/10.1109/TPAMI.2013.140.
ieee: C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification
for zero-shot learning of object categories,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013.
ista: Lampert C, Nickisch H, Harmeling S. 2013. Attribute-based classification for
zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 36(3), 453–465.
mla: Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning
of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.
short: C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis
and Machine Intelligence 36 (2013) 453–465.
date_created: 2018-12-11T11:58:08Z
date_published: 2013-07-30T00:00:00Z
date_updated: 2021-01-12T06:57:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2013.140
intvolume: ' 36'
issue: '3'
language:
- iso: eng
month: '07'
oa_version: None
page: 453 - 465
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '4385'
quality_controlled: '1'
scopus_import: 1
status: public
title: Attribute-based classification for zero-shot learning of object categories
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2013'
...
---
_id: '2520'
abstract:
- lang: eng
text: "We propose a probabilistic model to infer supervised latent variables in\r\nthe
Hamming space from observed data. Our model allows simultaneous\r\ninference of
the number of binary latent variables, and their values. The\r\nlatent variables
preserve neighbourhood structure of the data in a sense\r\nthat objects in the
same semantic concept have similar latent values, and\r\nobjects in different
concepts have dissimilar latent values. We formulate\r\nthe supervised infinite
latent variable problem based on an intuitive\r\nprinciple of pulling objects
together if they are of the same type, and\r\npushing them apart if they are not.
We then combine this principle with a\r\nflexible Indian Buffet Process prior
on the latent variables. We show that\r\nthe inferred supervised latent variables
can be directly used to perform a\r\nnearest neighbour search for the purpose
of retrieval. We introduce a new\r\napplication of dynamically extending hash
codes, and show how to\r\neffectively couple the structure of the hash codes with
continuously\r\ngrowing structure of the neighbourhood preserving infinite latent
feature\r\nspace."
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: David
full_name: Knowles, David
last_name: Knowles
- first_name: Zoubin
full_name: Ghahramani, Zoubin
last_name: Ghahramani
citation:
ama: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. The supervised IBP: Neighbourhood
preserving infinite latent feature models. In: Proceedings of the 29th Conference
Uncertainty in Artificial Intelligence. AUAI Press; 2013:527-536.'
apa: 'Quadrianto, N., Sharmanska, V., Knowles, D., & Ghahramani, Z. (2013).
The supervised IBP: Neighbourhood preserving infinite latent feature models. In
Proceedings of the 29th conference uncertainty in Artificial Intelligence
(pp. 527–536). Bellevue, WA, United States: AUAI Press.'
chicago: 'Quadrianto, Novi, Viktoriia Sharmanska, David Knowles, and Zoubin Ghahramani.
“The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.”
In Proceedings of the 29th Conference Uncertainty in Artificial Intelligence,
527–36. AUAI Press, 2013.'
ieee: 'N. Quadrianto, V. Sharmanska, D. Knowles, and Z. Ghahramani, “The supervised
IBP: Neighbourhood preserving infinite latent feature models,” in Proceedings
of the 29th conference uncertainty in Artificial Intelligence, Bellevue, WA,
United States, 2013, pp. 527–536.'
ista: 'Quadrianto N, Sharmanska V, Knowles D, Ghahramani Z. 2013. The supervised
IBP: Neighbourhood preserving infinite latent feature models. Proceedings of the
29th conference uncertainty in Artificial Intelligence. UAI: Uncertainty in Artificial
Intelligence, 527–536.'
mla: 'Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite
Latent Feature Models.” Proceedings of the 29th Conference Uncertainty in Artificial
Intelligence, AUAI Press, 2013, pp. 527–36.'
short: N. Quadrianto, V. Sharmanska, D. Knowles, Z. Ghahramani, in:, Proceedings
of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013,
pp. 527–536.
conference:
end_date: 2013-07-15
location: Bellevue, WA, United States
name: 'UAI: Uncertainty in Artificial Intelligence'
start_date: 2013-07-11
date_created: 2018-12-11T11:58:09Z
date_published: 2013-07-11T00:00:00Z
date_updated: 2023-02-23T10:46:36Z
day: '11'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
checksum: 325f20c4b926bd74d39006b97df572bd
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:16Z
date_updated: 2020-07-14T12:45:42Z
file_id: '5134'
file_name: IST-2013-137-v1+1_QuaShaKnoGha13.pdf
file_size: 1117100
relation: main_file
file_date_updated: 2020-07-14T12:45:42Z
has_accepted_license: '1'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Submitted Version
page: 527 - 536
publication: Proceedings of the 29th conference uncertainty in Artificial Intelligence
publication_identifier:
isbn:
- '9780974903996'
publication_status: published
publisher: AUAI Press
publist_id: '4381'
pubrep_id: '137'
quality_controlled: '1'
scopus_import: 1
status: public
title: 'The supervised IBP: Neighbourhood preserving infinite latent feature models'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2901'
abstract:
- lang: eng
text: ' We introduce the M-modes problem for graphical models: predicting the M
label configurations of highest probability that are at the same time local maxima
of the probability landscape. M-modes have multiple possible applications: because
they are intrinsically diverse, they provide a principled alternative to non-maximum
suppression techniques for structured prediction, they can act as codebook vectors
for quantizing the configuration space, or they can form component centers for
mixture model approximation. We present two algorithms for solving the M-modes
problem. The first algorithm solves the problem in polynomial time when the underlying
graphical model is a simple chain. The second algorithm solves the problem for
junction chains. In synthetic and real dataset, we demonstrate how M-modes can
improve the performance of prediction. We also use the generated modes as a tool
to understand the topography of the probability distribution of configurations,
for example with relation to the training set size and amount of noise in the
data. '
alternative_title:
- ' JMLR: W&CP'
author:
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Zhu
full_name: Yan, Zhu
last_name: Yan
- first_name: Dimitris
full_name: Metaxas, Dimitris
last_name: Metaxas
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable
modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.'
apa: 'Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013).
Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169).
Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence,
Scottsdale, AZ, United States: JMLR.'
chicago: Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph
Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69.
JMLR, 2013.
ieee: 'C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the
M most probable modes of a graphical model,” presented at the AISTATS: Conference
on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013,
vol. 31, pp. 161–169.'
ista: 'Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. 2013. Computing the M
most probable modes of a graphical model. AISTATS: Conference on Uncertainty
in Artificial Intelligence, JMLR: W&CP, vol. 31, 161–169.'
mla: Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model.
Vol. 31, JMLR, 2013, pp. 161–69.
short: C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013,
pp. 161–169.
conference:
end_date: 2013-05-01
location: Scottsdale, AZ, United States
name: ' AISTATS: Conference on Uncertainty in Artificial Intelligence'
start_date: 2013-04-29
date_created: 2018-12-11T12:00:14Z
date_published: 2013-01-01T00:00:00Z
date_updated: 2021-01-12T07:00:35Z
day: '01'
department:
- _id: HeEd
- _id: VlKo
- _id: ChLa
intvolume: ' 31'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://jmlr.org/proceedings/papers/v31/chen13a.html
month: '01'
oa: 1
oa_version: None
page: 161 - 169
publication_status: published
publisher: JMLR
publist_id: '3846'
quality_controlled: '1'
scopus_import: 1
status: public
title: Computing the M most probable modes of a graphical model
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 31
year: '2013'
...
---
_id: '2948'
abstract:
- lang: eng
text: 'Many visual datasets are traditionally used to analyze the performance of
different learning techniques. The evaluation is usually done within each dataset,
therefore it is questionable if such results are a reliable indicator of true
generalization ability. We propose here an algorithm to exploit the existing data
resources when learning on a new multiclass problem. Our main idea is to identify
an image representation that decomposes orthogonally into two subspaces: a part
specific to each dataset, and a part generic to, and therefore shared between,
all the considered source sets. This allows us to use the generic representation
as un-biased reference knowledge for a novel classification task. By casting the
method in the multi-view setting, we also make it possible to use different features
for different databases. We call the algorithm MUST, Multitask Unaligned Shared
knowledge Transfer. Through extensive experiments on five public datasets, we
show that MUST consistently improves the cross-datasets generalization performance.'
acknowledgement: This work was supported by the PASCAL 2 Network of Excellence (TT)
and by the Newton International Fellowship (NQ)
alternative_title:
- LNCS
author:
- first_name: Tatiana
full_name: Tommasi, Tatiana
last_name: Tommasi
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Barbara
full_name: Caputo, Barbara
last_name: Caputo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. Beyond dataset bias: Multi-task
unaligned shared knowledge transfer. 2013;7724:1-15. doi:10.1007/978-3-642-37331-2_1'
apa: 'Tommasi, T., Quadrianto, N., Caputo, B., & Lampert, C. (2013). Beyond
dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the
ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer. https://doi.org/10.1007/978-3-642-37331-2_1'
chicago: 'Tommasi, Tatiana, Novi Quadrianto, Barbara Caputo, and Christoph Lampert.
“Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer.” Lecture
Notes in Computer Science. Springer, 2013. https://doi.org/10.1007/978-3-642-37331-2_1.'
ieee: 'T. Tommasi, N. Quadrianto, B. Caputo, and C. Lampert, “Beyond dataset bias:
Multi-task unaligned shared knowledge transfer,” vol. 7724. Springer, pp. 1–15,
2013.'
ista: 'Tommasi T, Quadrianto N, Caputo B, Lampert C. 2013. Beyond dataset bias:
Multi-task unaligned shared knowledge transfer. 7724, 1–15.'
mla: 'Tommasi, Tatiana, et al. Beyond Dataset Bias: Multi-Task Unaligned Shared
Knowledge Transfer. Vol. 7724, Springer, 2013, pp. 1–15, doi:10.1007/978-3-642-37331-2_1.'
short: T. Tommasi, N. Quadrianto, B. Caputo, C. Lampert, 7724 (2013) 1–15.
conference:
end_date: 2012-11-09
location: Daejeon, Korea
name: 'ACCV: Asian Conference on Computer Vision'
start_date: 2012-11-05
date_created: 2018-12-11T12:00:30Z
date_published: 2013-04-04T00:00:00Z
date_updated: 2020-08-11T10:09:54Z
day: '04'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-37331-2_1
file:
- access_level: open_access
checksum: a0a7234a89e2192af655b0d0ae3bf445
content_type: application/pdf
creator: dernst
date_created: 2019-01-22T14:03:11Z
date_updated: 2020-07-14T12:45:55Z
file_id: '5874'
file_name: 2012_ACCV_Tommasi.pdf
file_size: 1513620
relation: main_file
file_date_updated: 2020-07-14T12:45:55Z
has_accepted_license: '1'
intvolume: ' 7724'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Submitted Version
page: 1 - 15
publication_status: published
publisher: Springer
publist_id: '3784'
quality_controlled: '1'
scopus_import: 1
series_title: Lecture Notes in Computer Science
status: public
title: 'Beyond dataset bias: Multi-task unaligned shared knowledge transfer'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7724
year: '2013'
...
---
_id: '3321'
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Quadrianto N, Lampert C. Kernel based learning. In: Dubitzky W, Wolkenhauer
O, Cho K, Yokota H, eds. Encyclopedia of Systems Biology. Vol 3. Springer;
2013:1069-1069. doi:10.1007/978-1-4419-9863-7_604'
apa: Quadrianto, N., & Lampert, C. (2013). Kernel based learning. In W. Dubitzky,
O. Wolkenhauer, K. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology
(Vol. 3, pp. 1069–1069). Springer. https://doi.org/10.1007/978-1-4419-9863-7_604
chicago: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” In Encyclopedia
of Systems Biology, edited by Werner Dubitzky, Olaf Wolkenhauer, Kwang Cho,
and Hiroki Yokota, 3:1069–1069. Springer, 2013. https://doi.org/10.1007/978-1-4419-9863-7_604.
ieee: N. Quadrianto and C. Lampert, “Kernel based learning,” in Encyclopedia
of Systems Biology, vol. 3, W. Dubitzky, O. Wolkenhauer, K. Cho, and H. Yokota,
Eds. Springer, 2013, pp. 1069–1069.
ista: 'Quadrianto N, Lampert C. 2013.Kernel based learning. In: Encyclopedia of
Systems Biology. vol. 3, 1069–1069.'
mla: Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.” Encyclopedia
of Systems Biology, edited by Werner Dubitzky et al., vol. 3, Springer, 2013,
pp. 1069–1069, doi:10.1007/978-1-4419-9863-7_604.
short: N. Quadrianto, C. Lampert, in:, W. Dubitzky, O. Wolkenhauer, K. Cho, H. Yokota
(Eds.), Encyclopedia of Systems Biology, Springer, 2013, pp. 1069–1069.
date_created: 2018-12-11T12:02:39Z
date_published: 2013-01-01T00:00:00Z
date_updated: 2021-01-12T07:42:38Z
day: '01'
department:
- _id: ChLa
doi: 10.1007/978-1-4419-9863-7_604
editor:
- first_name: Werner
full_name: Dubitzky, Werner
last_name: Dubitzky
- first_name: Olaf
full_name: Wolkenhauer, Olaf
last_name: Wolkenhauer
- first_name: Kwang
full_name: Cho, Kwang
last_name: Cho
- first_name: Hiroki
full_name: Yokota, Hiroki
last_name: Yokota
intvolume: ' 3'
language:
- iso: eng
month: '01'
oa_version: None
page: 1069 - 1069
publication: Encyclopedia of Systems Biology
publication_status: published
publisher: Springer
publist_id: '3314'
quality_controlled: '1'
status: public
title: Kernel based learning
type: encyclopedia_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2013'
...
---
_id: '2293'
abstract:
- lang: eng
text: Many computer vision problems have an asymmetric distribution of information
between training and test time. In this work, we study the case where we are given
additional information about the training data, which however will not be available
at test time. This situation is called learning using privileged information (LUPI).
We introduce two maximum-margin techniques that are able to make use of this additional
source of information, and we show that the framework is applicable to several
scenarios that have been studied in computer vision before. Experiments with attributes,
bounding boxes, image tags and rationales as additional information in object
classification show promising results.
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sharmanska V, Quadrianto N, Lampert C. Learning to rank using privileged information.
In: IEEE; 2013:825-832. doi:10.1109/ICCV.2013.107'
apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank
using privileged information (pp. 825–832). Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107'
chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Learning
to Rank Using Privileged Information,” 825–32. IEEE, 2013. https://doi.org/10.1109/ICCV.2013.107.
ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Learning to rank using privileged
information,” presented at the ICCV: International Conference on Computer Vision,
Sydney, Australia, 2013, pp. 825–832.'
ista: 'Sharmanska V, Quadrianto N, Lampert C. 2013. Learning to rank using privileged
information. ICCV: International Conference on Computer Vision, 825–832.'
mla: Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information.
IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107.
short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, IEEE, 2013, pp. 825–832.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2023-02-23T10:36:41Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.107
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: www.cv-foundation.org/openaccess/content_iccv_2013/papers/Sharmanska_Learning_to_Rank_2013_ICCV_paper.pdf
month: '12'
oa: 1
oa_version: Submitted Version
page: 825 - 832
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4635'
quality_controlled: '1'
scopus_import: 1
status: public
title: Learning to rank using privileged information
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2294'
abstract:
- lang: eng
text: "In this work we propose a system for automatic classification of Drosophila
embryos into developmental stages.\r\nWhile the system is designed to solve an
actual problem in biological research, we believe that the principle underly-\r\ning
it is interesting not only for biologists, but also for researchers in computer
vision. The main idea is to combine two orthogonal sources of information: one
is a classifier trained on strongly invariant features, which makes it applicable
to images of very different conditions, but also leads to rather noisy predictions.
The other is a label propagation step based on a more powerful similarity measure
that however is only consistent within specific subsets of the data at a time.\r\nIn
our biological setup, the information sources are the shape and the staining patterns
of embryo images. We show\r\nexperimentally that while neither of the methods
\ can be used by itself to achieve satisfactory results, their combina-\r\ntion
achieves prediction quality comparable to human performance."
author:
- first_name: Tomas
full_name: Kazmar, Tomas
last_name: Kazmar
- first_name: Evgeny
full_name: Kvon, Evgeny
last_name: Kvon
- first_name: Alexander
full_name: Stark, Alexander
last_name: Stark
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kazmar T, Kvon E, Stark A, Lampert C. Drosophila Embryo Stage Annotation using
Label Propagation. In: IEEE; 2013. doi:10.1109/ICCV.2013.139'
apa: 'Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo
Stage Annotation using Label Propagation. Presented at the ICCV: International
Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139'
chicago: Kazmar, Tomas, Evgeny Kvon, Alexander Stark, and Christoph Lampert. “Drosophila
Embryo Stage Annotation Using Label Propagation.” IEEE, 2013. https://doi.org/10.1109/ICCV.2013.139.
ieee: 'T. Kazmar, E. Kvon, A. Stark, and C. Lampert, “Drosophila Embryo Stage Annotation
using Label Propagation,” presented at the ICCV: International Conference on Computer
Vision, Sydney, Australia, 2013.'
ista: 'Kazmar T, Kvon E, Stark A, Lampert C. 2013. Drosophila Embryo Stage Annotation
using Label Propagation. ICCV: International Conference on Computer Vision.'
mla: Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation.
IEEE, 2013, doi:10.1109/ICCV.2013.139.
short: T. Kazmar, E. Kvon, A. Stark, C. Lampert, in:, IEEE, 2013.
conference:
end_date: 2013-12-08
location: Sydney, Australia
name: 'ICCV: International Conference on Computer Vision'
start_date: 2013-12-01
date_created: 2018-12-11T11:56:49Z
date_published: 2013-12-01T00:00:00Z
date_updated: 2021-01-12T06:56:35Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCV.2013.139
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.cv-foundation.org/openaccess/ICCV2013.py
month: '12'
oa: 1
oa_version: Submitted Version
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_status: published
publisher: IEEE
publist_id: '4634'
quality_controlled: '1'
scopus_import: 1
status: public
title: Drosophila Embryo Stage Annotation using Label Propagation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2013'
...
---
_id: '2825'
abstract:
- lang: eng
text: 'We study the problem of maximum marginal prediction (MMP) in probabilistic
graphical models, a task that occurs, for example, as the Bayes optimal decision
rule under a Hamming loss. MMP is typically performed as a two-stage procedure:
one estimates each variable''s marginal probability and then forms a prediction
from the states of maximal probability. In this work we propose a simple yet effective
technique for accelerating MMP when inference is sampling-based: instead of the
above two-stage procedure we directly estimate the posterior probability of each
decision variable. This allows us to identify the point of time when we are sufficiently
certain about any individual decision. Whenever this is the case, we dynamically
prune the variables we are confident about from the underlying factor graph. Consequently,
at any time only samples of variables whose decision is still uncertain need to
be created. Experiments in two prototypical scenarios, multi-label classification
and image inpainting, show that adaptive sampling can drastically accelerate MMP
without sacrificing prediction accuracy.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Dynamic pruning of factor graphs for maximum marginal prediction.
In: Vol 1. Neural Information Processing Systems; 2012:82-90.'
apa: 'Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal
prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing
Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.'
chicago: Lampert, Christoph. “Dynamic Pruning of Factor Graphs for Maximum Marginal
Prediction,” 1:82–90. Neural Information Processing Systems, 2012.
ieee: 'C. Lampert, “Dynamic pruning of factor graphs for maximum marginal prediction,”
presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV,
United States, 2012, vol. 1, pp. 82–90.'
ista: 'Lampert C. 2012. Dynamic pruning of factor graphs for maximum marginal prediction.
NIPS: Neural Information Processing Systems vol. 1, 82–90.'
mla: Lampert, Christoph. Dynamic Pruning of Factor Graphs for Maximum Marginal
Prediction. Vol. 1, Neural Information Processing Systems, 2012, pp. 82–90.
short: C. Lampert, in:, Neural Information Processing Systems, 2012, pp. 82–90.
conference:
end_date: 2012-12-06
location: Lake Tahoe, NV, United States
name: 'NIPS: Neural Information Processing Systems'
start_date: 2012-12-03
date_created: 2018-12-11T11:59:48Z
date_published: 2012-12-01T00:00:00Z
date_updated: 2021-01-12T06:59:59Z
day: '01'
department:
- _id: ChLa
intvolume: ' 1'
language:
- iso: eng
month: '12'
oa_version: None
page: 82 - 90
publication_status: published
publisher: Neural Information Processing Systems
publist_id: '3975'
quality_controlled: '1'
scopus_import: 1
status: public
title: Dynamic pruning of factor graphs for maximum marginal prediction
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 1
year: '2012'
...
---
_id: '2915'
acknowledgement: "The project receives funding from the European Community’s Seventh
Framework Programme under grant agreement\r\nno. ICT- 248273 GeRT."
article_processing_charge: No
author:
- first_name: Oliver
full_name: Kroemer, Oliver
last_name: Kroemer
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Jan
full_name: Peters, Jan
last_name: Peters
citation:
ama: 'Kroemer O, Lampert C, Peters J. Multi-modal learning for dynamic tactile sensing.
In: Deutsches Zentrum für Luft und Raumfahrt; 2012.'
apa: Kroemer, O., Lampert, C., & Peters, J. (2012). Multi-modal learning for
dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt.
chicago: Kroemer, Oliver, Christoph Lampert, and Jan Peters. “Multi-Modal Learning
for Dynamic Tactile Sensing.” Deutsches Zentrum für Luft und Raumfahrt, 2012.
ieee: O. Kroemer, C. Lampert, and J. Peters, “Multi-modal learning for dynamic tactile
sensing,” 2012.
ista: Kroemer O, Lampert C, Peters J. 2012. Multi-modal learning for dynamic tactile
sensing
mla: Kroemer, Oliver, et al. Multi-Modal Learning for Dynamic Tactile Sensing.
Deutsches Zentrum für Luft und Raumfahrt, 2012.
short: O. Kroemer, C. Lampert, J. Peters, in:, Deutsches Zentrum für Luft und Raumfahrt,
2012.
date_created: 2018-12-11T12:00:19Z
date_published: 2012-10-11T00:00:00Z
date_updated: 2023-10-17T07:58:59Z
day: '11'
department:
- _id: ChLa
language:
- iso: eng
month: '10'
oa_version: None
publication_status: published
publisher: Deutsches Zentrum für Luft und Raumfahrt
publist_id: '3828'
quality_controlled: '1'
status: public
title: Multi-modal learning for dynamic tactile sensing
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '3124'
abstract:
- lang: eng
text: "We consider the problem of inference in a graphical model with binary variables.
While in theory it is arguably preferable to compute marginal probabilities, in
practice researchers often use MAP inference due to the availability of efficient
discrete optimization algorithms. We bridge the gap between the two approaches
by introducing the Discrete Marginals technique in which approximate marginals
are obtained by minimizing an objective function with unary and pairwise terms
over a discretized domain. This allows the use of techniques originally developed
for MAP-MRF inference and learning. We explore two ways to set up the objective
function - by discretizing the Bethe free energy and by learning it from training
data. Experimental results show that for certain types of graphs a learned function
can outperform the Bethe approximation. We also establish a link between the Bethe
free energy and submodular functions.\r\n"
alternative_title:
- Inferning 2012
author:
- first_name: Filip
full_name: Korc, Filip
id: 476A2FD6-F248-11E8-B48F-1D18A9856A87
last_name: Korc
- first_name: Vladimir
full_name: Kolmogorov, Vladimir
id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
last_name: Kolmogorov
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Korc F, Kolmogorov V, Lampert C. Approximating marginals using discrete energy
minimization. In: ICML; 2012.'
apa: 'Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals
using discrete energy minimization. Presented at the ICML: International Conference
on Machine Learning, Edinburgh, Scotland: ICML.'
chicago: Korc, Filip, Vladimir Kolmogorov, and Christoph Lampert. “Approximating
Marginals Using Discrete Energy Minimization.” ICML, 2012.
ieee: 'F. Korc, V. Kolmogorov, and C. Lampert, “Approximating marginals using discrete
energy minimization,” presented at the ICML: International Conference on Machine
Learning, Edinburgh, Scotland, 2012.'
ista: 'Korc F, Kolmogorov V, Lampert C. 2012. Approximating marginals using discrete
energy minimization. ICML: International Conference on Machine Learning, Inferning
2012, .'
mla: Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization.
ICML, 2012.
short: F. Korc, V. Kolmogorov, C. Lampert, in:, ICML, 2012.
conference:
end_date: 2012-07-01
location: Edinburgh, Scotland
name: 'ICML: International Conference on Machine Learning'
start_date: 2012-06-26
date_created: 2018-12-11T12:01:31Z
date_published: 2012-06-30T00:00:00Z
date_updated: 2023-02-23T12:24:24Z
day: '30'
ddc:
- '000'
department:
- _id: ChLa
- _id: VlKo
file:
- access_level: open_access
checksum: 3d0d4246548c736857302aadb2ff5d15
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:11:34Z
date_updated: 2020-07-14T12:46:00Z
file_id: '4889'
file_name: IST-2016-565-v1+1_DM-inferning2012.pdf
file_size: 305836
relation: main_file
file_date_updated: 2020-07-14T12:46:00Z
has_accepted_license: '1'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Submitted Version
publication_status: published
publisher: ICML
publist_id: '3575'
pubrep_id: '565'
quality_controlled: '1'
related_material:
record:
- id: '5396'
relation: later_version
status: public
status: public
title: Approximating marginals using discrete energy minimization
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '3125'
abstract:
- lang: eng
text: We propose a new learning method to infer a mid-level feature representation
that combines the advantage of semantic attribute representations with the higher
expressive power of non-semantic features. The idea lies in augmenting an existing
attribute-based representation with additional dimensions for which an autoencoder
model is coupled with a large-margin principle. This construction allows a smooth
transition between the zero-shot regime with no training example, the unsupervised
regime with training examples but without class labels, and the supervised regime
with training examples and with class labels. The resulting optimization problem
can be solved efficiently, because several of the necessity steps have closed-form
solutions. Through extensive experiments we show that the augmented representation
achieves better results in terms of object categorization accuracy than the semantic
representation alone.
alternative_title:
- LNCS
article_processing_charge: No
author:
- first_name: Viktoriia
full_name: Sharmanska, Viktoriia
id: 2EA6D09E-F248-11E8-B48F-1D18A9856A87
last_name: Sharmanska
orcid: 0000-0003-0192-9308
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sharmanska V, Quadrianto N, Lampert C. Augmented attribute representations.
In: Vol 7576. Springer; 2012:242-255. doi:10.1007/978-3-642-33715-4_18'
apa: 'Sharmanska, V., Quadrianto, N., & Lampert, C. (2012). Augmented attribute
representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference
on Computer Vision, Florence, Italy: Springer. https://doi.org/10.1007/978-3-642-33715-4_18'
chicago: Sharmanska, Viktoriia, Novi Quadrianto, and Christoph Lampert. “Augmented
Attribute Representations,” 7576:242–55. Springer, 2012. https://doi.org/10.1007/978-3-642-33715-4_18.
ieee: 'V. Sharmanska, N. Quadrianto, and C. Lampert, “Augmented attribute representations,”
presented at the ECCV: European Conference on Computer Vision, Florence, Italy,
2012, vol. 7576, no. PART 5, pp. 242–255.'
ista: 'Sharmanska V, Quadrianto N, Lampert C. 2012. Augmented attribute representations.
ECCV: European Conference on Computer Vision, LNCS, vol. 7576, 242–255.'
mla: Sharmanska, Viktoriia, et al. Augmented Attribute Representations. Vol.
7576, no. PART 5, Springer, 2012, pp. 242–55, doi:10.1007/978-3-642-33715-4_18.
short: V. Sharmanska, N. Quadrianto, C. Lampert, in:, Springer, 2012, pp. 242–255.
conference:
end_date: 2012-10-13
location: Florence, Italy
name: 'ECCV: European Conference on Computer Vision'
start_date: 2012-10-07
date_created: 2018-12-11T12:01:32Z
date_published: 2012-10-01T00:00:00Z
date_updated: 2023-02-23T11:13:25Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1007/978-3-642-33715-4_18
file:
- access_level: open_access
checksum: bccdbe0663780d25a1e0524002b2d896
content_type: application/pdf
creator: dernst
date_created: 2020-05-15T12:29:04Z
date_updated: 2020-07-14T12:46:00Z
file_id: '7861'
file_name: 2012_ECCV_Sharmanska.pdf
file_size: 6073897
relation: main_file
file_date_updated: 2020-07-14T12:46:00Z
has_accepted_license: '1'
intvolume: ' 7576'
issue: PART 5
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 242 - 255
publication_status: published
publisher: Springer
publist_id: '3574'
quality_controlled: '1'
scopus_import: 1
status: public
title: Augmented attribute representations
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7576
year: '2012'
...
---
_id: '3126'
abstract:
- lang: eng
text: "In this work we propose a new information-theoretic clustering algorithm
that infers cluster memberships by direct optimization of a non-parametric mutual
information estimate between data distribution and cluster assignment. Although
the optimization objective has a solid theoretical foundation it is hard to optimize.
We propose an approximate optimization formulation that leads to an efficient
algorithm with low runtime complexity. The algorithm has a single free parameter,
the number of clusters to find. We demonstrate superior performance on several
synthetic and real datasets.\r\n"
alternative_title:
- LNCS
author:
- first_name: Andreas
full_name: Müller, Andreas
last_name: Müller
- first_name: Sebastian
full_name: Nowozin, Sebastian
last_name: Nowozin
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal
spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:10.1007/978-3-642-32717-9_21'
apa: 'Müller, A., Nowozin, S., & Lampert, C. (2012). Information theoretic clustering
using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM:
German Association For Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_21'
chicago: Müller, Andreas, Sebastian Nowozin, and Christoph Lampert. “Information
Theoretic Clustering Using Minimal Spanning Trees,” 7476:205–15. Springer, 2012.
https://doi.org/10.1007/978-3-642-32717-9_21.
ieee: 'A. Müller, S. Nowozin, and C. Lampert, “Information theoretic clustering
using minimal spanning trees,” presented at the DAGM: German Association For Pattern
Recognition, Graz, Austria, 2012, vol. 7476, pp. 205–215.'
ista: 'Müller A, Nowozin S, Lampert C. 2012. Information theoretic clustering using
minimal spanning trees. DAGM: German Association For Pattern Recognition, LNCS,
vol. 7476, 205–215.'
mla: Müller, Andreas, et al. Information Theoretic Clustering Using Minimal Spanning
Trees. Vol. 7476, Springer, 2012, pp. 205–15, doi:10.1007/978-3-642-32717-9_21.
short: A. Müller, S. Nowozin, C. Lampert, in:, Springer, 2012, pp. 205–215.
conference:
end_date: 2012-08-31
location: Graz, Austria
name: 'DAGM: German Association For Pattern Recognition'
start_date: 2012-08-28
date_created: 2018-12-11T12:01:32Z
date_published: 2012-08-14T00:00:00Z
date_updated: 2021-01-12T07:41:14Z
day: '14'
department:
- _id: ChLa
doi: 10.1007/978-3-642-32717-9_21
intvolume: ' 7476'
language:
- iso: eng
month: '08'
oa_version: None
page: 205 - 215
publication_status: published
publisher: Springer
publist_id: '3573'
quality_controlled: '1'
scopus_import: 1
status: public
title: Information theoretic clustering using minimal spanning trees
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 7476
year: '2012'
...
---
_id: '3127'
abstract:
- lang: eng
text: "When searching for characteristic subpatterns in potentially noisy graph
data, it appears self-evident that having multiple observations would be better
than having just one. However, it turns out that the inconsistencies introduced
when different graph instances have different edge sets pose a serious challenge.
In this work we address this challenge for the problem of finding maximum weighted
cliques.\r\n We introduce the concept of most persistent soft-clique. This
is subset of vertices, that 1) is almost fully or at least densely connected,
2) occurs in all or almost all graph instances, and 3) has the maximum weight.
We present a measure of clique-ness, that essentially counts the number of edge
missing to make a subset of vertices into a clique. With this measure, we show
that the problem of finding the most persistent soft-clique problem can be cast
either as: a) a max-min two person game optimization problem, or b) a min-min
soft margin optimization problem. Both formulations lead to the same solution
when using a partial Lagrangian method to solve the optimization problems. By
experiments on synthetic data and on real social network data, we show that the
proposed method is able to reliably find soft cliques in graph data, even if that
is distorted by random noise or unreliable observations."
article_processing_charge: No
author:
- first_name: Novi
full_name: Quadrianto, Novi
last_name: Quadrianto
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Chao
full_name: Chen, Chao
id: 3E92416E-F248-11E8-B48F-1D18A9856A87
last_name: Chen
citation:
ama: 'Quadrianto N, Lampert C, Chen C. The most persistent soft-clique in a set
of sampled graphs. In: Proceedings of the 29th International Conference on
Machine Learning. ML Research Press; 2012:211-218.'
apa: 'Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique
in a set of sampled graphs. In Proceedings of the 29th International Conference
on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: ML Research
Press.'
chicago: Quadrianto, Novi, Christoph Lampert, and Chao Chen. “The Most Persistent
Soft-Clique in a Set of Sampled Graphs.” In Proceedings of the 29th International
Conference on Machine Learning, 211–18. ML Research Press, 2012.
ieee: N. Quadrianto, C. Lampert, and C. Chen, “The most persistent soft-clique in
a set of sampled graphs,” in Proceedings of the 29th International Conference
on Machine Learning, Edinburgh, United Kingdom, 2012, pp. 211–218.
ista: 'Quadrianto N, Lampert C, Chen C. 2012. The most persistent soft-clique in
a set of sampled graphs. Proceedings of the 29th International Conference on Machine
Learning. ICML: International Conference on Machine Learning, 211–218.'
mla: Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled
Graphs.” Proceedings of the 29th International Conference on Machine Learning,
ML Research Press, 2012, pp. 211–18.
short: N. Quadrianto, C. Lampert, C. Chen, in:, Proceedings of the 29th International
Conference on Machine Learning, ML Research Press, 2012, pp. 211–218.
conference:
end_date: 2012-07-01
location: Edinburgh, United Kingdom
name: 'ICML: International Conference on Machine Learning'
start_date: 2012-06-26
date_created: 2018-12-11T12:01:33Z
date_published: 2012-06-01T00:00:00Z
date_updated: 2023-10-17T11:55:06Z
day: '01'
department:
- _id: ChLa
- _id: HeEd
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1206.4652
month: '06'
oa: 1
oa_version: Preprint
page: 211-218
publication: Proceedings of the 29th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
publist_id: '3572'
quality_controlled: '1'
scopus_import: '1'
status: public
title: The most persistent soft-clique in a set of sampled graphs
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2012'
...